Roles of environmental and spatial factors in structuring assemblages of forest-floor mesostigmata in the boreal region of Northern Alberta, Canada
Bibliographic record
Abstract
Mesostigmatid mites (Arachnida: Parasitiformes) are a diverse, abundant group of soil predators; however, in comparison to some other groups of soil fauna (e.g. oribatid mites), little is known about environmental and spatial processes that influence local species richness and abundance. The main objective of our study was to identify factors strongly correlated with assemblages of Mesostigmata from boreal forests in northern Alberta, Canada. Soil and litter samples came from 62 sites with up to 4 samples per site (216 samples total), with a north–south range of ~800 km between sites. Environmental variables included ground cover type (e.g. moss, lichen, grass), disturbance intensity, precipitation, and temperature. From 3021 individual adult Mesostigmata, we identified 101 species/morphospecies from 21 families, the most species-rich being Ascidae, Zerconidae, and Digamasellidae. Redundancy analysis determined that the environmental variables that correlated most strongly with mite assemblages were precipitation, moss cover, and disturbance intensity. Spatial distance between sites had almost the same explanatory power, as environmental factors. Assemblages became more dissimilar with increasing spatial distance. This study showed the importance that particular environmental and spatial factors likely have on Mesostigmata composition in Canadian boreal forests; however, manipulative studies are required to be able to attribute causation to these correlations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".